An open-source library for data mining and data analysis. This package implements various methods of machine learning such as supervised learning (data classification, data regression, etc.), unsupervised learning (data clustering, etc.), and data pre-processing. This package is implemented on Python numerical libraries, NumPy and Scipy, and supports parallel computation.
An open source application to simulate crystal structures and to calculate and refine against diffraction pattern and the pair distribution function. A special emphasis placed is on the simulation of materials with disorder and the package provides many tools to create and distribute defects throughout the crystal. Another strong feature is the simulation of nanoparticles.
An open-source multi-purpose application for many-particle simulation. This application prepares various kinds of statistical methods and potentials, and can perform simulation of rigid-body mechanics, Langevin dynamics, dissipative-particle dynamics, nonequilibrium molecular dynamics, and so on. It prepares python scripts for production of initial conditions, job submission, and analysis of results.
An application for data analysis of X-ray absorption fine structure (XAFS). By interactive operation using a command line, experimental data of XAFS can be analyzed by various analysis methods. This application also supports various useful functions such as high-speed Fourier analysis, fitting in the radial/k-space coordinates, and data plotting.
An open-source Python package for calculation of quantum transport properties. Based on tight-binding models, this application can perform high-speed calculation of various transport properties such as conductance, current noise, and density of states. It can describe geometries of physical systems flexibly and easily, and can also treat superconductors, ferromagnetic materials, topological matters, and graphene.
ALPS is a numerical simulation library for strongly correlated systems such as magnetic materials or correlated electrons. It contains typicalsolvers for strongly correlated systems: Monte Carlo methods, exact diagonalization, the density matrix renormalization group, etc. It can be used to calculate heat capacities, susceptibilities, magnetization processes in interacting spin systems, the density of states in strongly correlated electrons, etc. A highly efficient scheduler for parallel computing is another improvement.
※Related links are temporary changed due to the server maintenance for ALPS project.
Open-source package for molecular dynamics simulation designed for biological macromolecules. This package can perform molecular dynamics simulation of biological macromolecules such as proteins, lipids, and nuclear acids as well as solutions by controlling temperature and pressure. This package can treat long-range interaction and free energy, and is designed for parallel computing.
Payware for quantum chemical calculation based on the density functional theory. This application supports relativistic effects needed in treatment of transition-metal complexes and heavy elements, and can also treat effect of solvents with the method of COSMO and 3D-RISM. In addition to ordinal optical spectra, it can evaluate various spectra data such as NMR, atomic vibration, electron spin resonance, and nuclear quadrupole resonance (NQR).
Commercially-available free software for Computer-Aided Drug Development. It includes programs for compound database, protein-compound docking, structure-based drug screening, ligand-based drug screening, protein-ligand binding site prediction, molecular editor, physical property prediction, synthetic accessibility prediction, thermodynamic calculation including multi-canonical dynamics, and molecular dynamics simulations with and without acceleration using GPUs and MPI parallelization.
A numerical library for machine learning. Various functions on machine learning (including supervised learning and unsupervised learning) are implemented in this package. Complex network can be expressed in a simple form by using data flow graphs. Efficient CPU/GPGPU parallel computation is supported to realise efficient operation on large scale data.